Sales and Marketing, Research & Development (R&D), Supply Chain Management (SCM) including distribution, Workplace Management and Operations are where advanced analytics including Big Data are making the greatest contributions to revenue growth today.

The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables.

Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US.

“Nothing that you will learn in the course of your studies will be of the slightest possible use to you,” the Oxford philosophy professor John Alexander Smith told his students, in 1914, “save only this: if you work hard and intelligently, you should be able to detect when a man is talking rot

In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”. While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014.

It feels good to be a data geek in 2017. Last year, we asked “Is Big Data Still a Thing?”, observing that since Big Data is largely “plumbing”, it has been subject to enterprise adoption cycles that are much slower than the hype cycle.

Ethnography Matters, I’m republishing the post for the launch of the new Ethnography Matters Medium channel. I’ve updated the article with a case study from my time at Nokia where I witnessed their over-dependence on quantitative data.

Recently, three economists—Oded Netzer and Alain Lemaire, both of Columbia, and Michal Herzenstein of the University of Delaware—looked for ways to predict the likelihood of whether a borrower would pay back a loan. The scholars used data from Prosper, a peer-to-peer lending site.

Today’s headlines are filled with technological breakthroughs that promise an optimized future, from artificial intelligence to diagnose disease to self-driving cars that revolutionize transportation. One day, everything will be easier, faster, and better, we’re told.

Almost everyone can agree that big data has taken the business world by storm, but what’s next? Will data continue to grow? What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? fast data? — appears on the horizon.

The world is getting more technical, and the jobs of the future are increasingly going to require you to be able to code across multiple programming languages, understand network security, and work comfortably with big data.

The legal system generates a huge and ever-increasing amount of data. Each new case brought to court (and there are 350,000 in the US alone each year) increases the body of knowledge that a lawyer has to get to grips with to do their job.

The term "Big Data" has launched a veritable industry of processes, personnel and technology to support what appears to be an exploding new field. Giant companies like Amazon and Wal-Mart as well as bodies such as the U.S.

Big Data started with algorithms helpfully scouring vast amounts of data to find patterns. These days it feels a bit like Big Brother. Using machine learning and AI to tweak algorithms, companies are now able to deliver profound insights from datasets once considered impossible to compile.

It is widely forecasted that a shortage of skills in data science and analytics will mean a great deal of money is wasted through missed opportunities in coming years. Traditional academic establishments have begun to move to fill the gap.

The Hollywood film Moneyball (2011) is about the Oakland Athletics baseball team and the attempt by its manager to put together a competitive team on a lean budget using data and computer analytics rather than depending on mere biases to recruit new players.

GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking. Mo Zhou was snapped up by I.B.M. last summer, as a freshly minted Yale M.B.A., to join the technology company’s fast-growing ranks of data consultants.

It’s just possible that there is a looming crisis in yet another technological sector whose proponents have leaped too far ahead, and too soon, promising all kinds of things they are unable to deliver.

Will the ascent of artificial intelligence (AI) and machine learning built by big data create an unstoppable inequality of innovation? Last week we interviewed veteran investor Bill Janeway, and it was fascinating.

Vikingmaiden88 is twenty-six years old. She enjoys reading history and writing poetry. Her signature quote is from Shakespeare. I gleaned all this from her profile and posts on Stormfront.org, America’s most popular online hate site.

The explosion of hype around the term “big data” ushered in a rabid desire in companies big and small to get their hands on employees with a data science skill set. For evidence, you need look no further than Indeed’s graph of the number of big data-related job postings:

Air travel plunged after September 11th 2001. Which is understandable. Everyone saw videos of the planes hitting the towers. News stations put the clips on repeat for months. Add to it the consensus that a follow-up attack was a matter of when, not if, and people felt reckless approaching airplanes.

Cukier makes the point that no area of human endeavour or industrial sector will be immune from the complete shakeup that Big Data is about to bring, as it transforms society, politics, and business. As he says neatly “More isn’t just more. More is new. More is better. More is different.

For organizations of all sizes, data management has shifted from an important competency to a critical differentiator that can determine market winners and has-beens. Fortune 1000 companies and government bodies are starting to benefit from the innovations of the web pioneers.

The field of big data is quite vast and it can be a very daunting task for anyone who starts learning big data & its related technologies. The big data technologies are numerous and it can be overwhelming to decide from where to begin. This is the reason I thought of writing this article.

Once upon a time, in the Pony Expresso cafe in Seattle, a man and a woman began to experience the long-mysterious but increasingly scientifically investigated thing we call love. The first stage is called "limerence.

At the end of last year I started exploring issues surrounding Big Data from a political standpoint. The first essay summarized the history of Big Data in terms of interventions over marginalized groups.

Don’t expect a joint statement of solidarity, but conservatives balking at a social science-driven administrative state and liberals flailing against Big Data on privacy and civil rights grounds may be approaching a Stanley and Livingstone moment, arriving at the same clearing in the ideological

For a company to succeed, it is essential that their practices are data-driven. For many companies, that means harnessing insights from Big Data. Addressing this issue head-on is Harvard-incubated Experfy.

The field of Big Data requires more clarity and I am a big fan of simple explanations. This is why I have attempted to provide simple explanations for some of the most important technologies and terms you will come across if you’re looking at getting into big data.

If I claimed that Americans have gotten more self-centered lately, you might just chalk me up as a curmudgeon, prone to good-ol’-days whining. But what if I said I could back that claim up by analyzing 150 billion words of text? A few decades ago, evidence on such a scale was a pipe dream.

The digital revolution is in full swing. How will it change our world? The amount of data we produce doubles every year. In other words: in 2016 we produced as much data as in the entire history of humankind through 2015.

Big Data is changing the way we do science today. Traditionally, data were collected manually by scientists making measurements, using microscopes or surveys. These data could be analyzed by hand or using simple statistical software on a PC. Big Data has changed all that.

Big Data is powerful on its own. So is artificial intelligence. What happens when the two are merged? Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade.

There are very few industries where Big Data isn’t a hot topic right now. Businesses are collecting and analyzing ever-growing amounts of data in their quest for increased efficiency, reduced waste and, of course, profits.

We live in what is sometimes called the ‘petabyte era’, and this pronouncement has provoked much discussion of the sheer size of data stores being created, as well as their rapid growth. Claims circulate along the lines of: ‘Every day, we create 2.

In this post I outline my how Uber uses big data analytics to drive business success. The post was first published in my column for Data Science Central. Uber is a smartphone-app based taxi booking service which connects users who need to get somewhere with drivers willing to give them a ride.

Gentrification of neighborhoods can wreak havoc for those most vulnerable to change. Sure, access to services and amenities rise in a gentrifying neighborhood. That is a good thing. But those amenities won't do you much good if you're forced to move because of skyrocketing housing costs.

A few weeks ago, I wrote about big opportunities in Little Data. And while my stance on Little Data hasn’t changed, there are also very exciting and very surprising things happening with Big Data right now.

Historically, when new technologies become easier to use, they transform industries. That’s what’s happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc.

According to a survey by Silicon Valley Bank, 90 percent of startups believe finding talent is their biggest challenge. Yet, a solution to this problem could lie with Big Data -- massive amounts of structured and unstructured data that's difficult to process using traditional techniques.

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications.

This looks to be the year that we reach peak big data hype. From wildly popular big data conferences to columns in major newspapers, the business and science worlds are focused on how large datasets can give insight on previously intractable challenges.

Now that many executives are finding measurable results from their Big Data initiatives, they are looking ahead and making decisions about investments in emerging capabilities such as artificial intelligence and machine learning.

More consolidation in the so-called big data space. Publicly-listed U.S. big data company Teradata has acquired London-based Big Data Partnership, a startup that provides big data solutions and training to help companies become more savvy in the use of, well, big data.

Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these